{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Node classification with Node2Vec using Stellargraph components"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbsphinx": "hidden",
    "tags": [
     "CloudRunner"
    ]
   },
   "source": [
    "<table><tr><td>Run the latest release of this notebook:</td><td><a href=\"https://mybinder.org/v2/gh/stellargraph/stellargraph/master?urlpath=lab/tree/demos/node-classification/keras-node2vec-node-classification.ipynb\" alt=\"Open In Binder\" target=\"_parent\"><img src=\"https://mybinder.org/badge_logo.svg\"/></a></td><td><a href=\"https://colab.research.google.com/github/stellargraph/stellargraph/blob/master/demos/node-classification/keras-node2vec-node-classification.ipynb\" alt=\"Open In Colab\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\"/></a></td></tr></table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This example demonstrates how to perform node classification with Node2Vec using the Stellargraph components. This uses a Keras implementation of Node2Vec available in stellargraph instead of the reference implementation provided by ``gensim``.\n",
    "\n",
    "<a name=\"refs\"></a>\n",
    "**References**\n",
    "\n",
    "[1] Node2Vec: Scalable Feature Learning for Networks. A. Grover, J. Leskovec. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2016. ([link](https://snap.stanford.edu/node2vec/))\n",
    "\n",
    "[2] Distributed representations of words and phrases and their compositionality. T. Mikolov, I. Sutskever, K. Chen, G. S. Corrado, and J. Dean.  In Advances in Neural Information Processing Systems (NIPS), pp. 3111-3119, 2013. ([link](https://papers.nips.cc/paper/5021-distributed-representations-of-words-and-phrases-and-their-compositionality.pdf))\n",
    "\n",
    "[3] word2vec Parameter Learning Explained. X. Rong. arXiv preprint arXiv:1411.2738. 2014 Nov 11. ([link](https://arxiv.org/pdf/1411.2738.pdf))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction\n",
    "Following word2vec [2,3], for each (``target``,``context``) node pair $(v_i,v_j)$ collected from random walks,  we learn the representation for the target node $v_i$ by using it to predict the existence of context node $v_j$, with the following three-layer neural network."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](word2vec-illustration.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Node $v_i$'s representation in the hidden layer is obtained by multiplying $v_i$'s one-hot representation in the input layer with the input-to-hidden weight matrix $W_{in}$, which is equivalent to look up the $i$th row of input-to-hidden weight matrix $W_{in}$. The existence probability of each node conditioned on node $v_i$ is outputted in the output layer, which is obtained by multiplying $v_i$'s hidden-layer representation with the hidden-to-out weight matrix $W_{out}$ followed by a softmax activation. To capture the ``target-context`` relation between $v_i$ and $v_j$, we need to maximize the probability $\\mathrm{P}(v_j|v_i)$. However, computing $\\mathrm{P}(v_j|v_i)$ is time consuming, which involves the matrix multiplication between $v_i$'s hidden-layer representation and the hidden-to-out weight matrix $W_{out}$. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To speed up the computing, we adopt the negative sampling strategy [2,3]. For each (``target``, ``context``) node pair, we sample a negative node $v_k$, which is not $v_i$'s context. To obtain the output, instead of multiplying $v_i$'s hidden-layer representation with the hidden-to-out weight matrix $W_{out}$ followed by a softmax activation, we only calculate the dot product between $v_i$'s hidden-layer representation and the $j$th column as well as the $k$th column of the hidden-to-output weight matrix $W_{out}$ followed by a sigmoid activation respectively. According to [3], the original objective to maximize $\\mathrm{P}(v_j|v_i)$ can be approximated by minimizing the cross entropy between $v_j$ and $v_k$'s outputs and their ground-truth labels (1 for $v_j$ and 0 for $v_k$)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Following [2,3], we denote the rows of the input-to-hidden weight matrix $W_{in}$ as ``input_embeddings`` and the columns of the hidden-to-out weight matrix $W_{out}$ as ``output_embeddings``. To build the Node2Vec model, we need look up ``input_embeddings`` for target nodes and ``output_embeddings`` for context nodes and calculate their inner product together with a sigmoid activation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "nbsphinx": "hidden",
    "tags": [
     "CloudRunner"
    ]
   },
   "outputs": [],
   "source": [
    "# install StellarGraph if running on Google Colab\n",
    "import sys\n",
    "if 'google.colab' in sys.modules:\n",
    "  %pip install -q stellargraph[demos]==1.2.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "nbsphinx": "hidden",
    "tags": [
     "VersionCheck"
    ]
   },
   "outputs": [],
   "source": [
    "# verify that we're using the correct version of StellarGraph for this notebook\n",
    "import stellargraph as sg\n",
    "\n",
    "try:\n",
    "    sg.utils.validate_notebook_version(\"1.2.1\")\n",
    "except AttributeError:\n",
    "    raise ValueError(\n",
    "        f\"This notebook requires StellarGraph version 1.2.1, but a different version {sg.__version__} is installed.  Please see <https://github.com/stellargraph/stellargraph/issues/1172>.\"\n",
    "    ) from None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.manifold import TSNE\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegressionCV\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "import os\n",
    "import networkx as nx\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from tensorflow import keras\n",
    "\n",
    "from stellargraph import StellarGraph\n",
    "from stellargraph.data import BiasedRandomWalk\n",
    "from stellargraph.data import UnsupervisedSampler\n",
    "from stellargraph.data import BiasedRandomWalk\n",
    "from stellargraph.mapper import Node2VecLinkGenerator, Node2VecNodeGenerator\n",
    "from stellargraph.layer import Node2Vec, link_classification\n",
    "\n",
    "from stellargraph import datasets\n",
    "from IPython.display import display, HTML\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dataset\n",
    "\n",
    "\n",
    "For clarity, we use only the largest connected component, ignoring isolated nodes and subgraphs; having these in the data does not prevent the algorithm from running and producing valid results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "The Cora dataset consists of 2708 scientific publications classified into one of seven classes. The citation network consists of 5429 links. Each publication in the dataset is described by a 0/1-valued word vector indicating the absence/presence of the corresponding word from the dictionary. The dictionary consists of 1433 unique words."
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset = datasets.Cora()\n",
    "display(HTML(dataset.description))\n",
    "G, subjects = dataset.load(largest_connected_component_only=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "StellarGraph: Undirected multigraph\n",
      " Nodes: 2485, Edges: 5209\n",
      "\n",
      " Node types:\n",
      "  paper: [2485]\n",
      "    Features: float32 vector, length 1433\n",
      "    Edge types: paper-cites->paper\n",
      "\n",
      " Edge types:\n",
      "    paper-cites->paper: [5209]\n",
      "        Weights: all 1 (default)\n",
      "        Features: none\n"
     ]
    }
   ],
   "source": [
    "print(G.info())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The Node2Vec algorithm\n",
    "\n",
    "The Node2Vec algorithm introduced in [[1]](#refs) is a 2-step representation learning algorithm. The two steps are:\n",
    "\n",
    "1. Use random walks to generate sentences from a graph. A sentence is a list of node ids. The set of all sentences makes a corpus.\n",
    "\n",
    "2. The corpus is then used to learn an embedding vector for each node in the graph. Each node id is considered a unique word/token in a dictionary that has size equal to the number of nodes in the graph. The Word2Vec algorithm [[2]](#refs) is used for calculating the embedding vectors.\n",
    "\n",
    "In this implementation, we train the Node2Vec algorithm in the following two steps:\n",
    "\n",
    "1. Generate a set of (`target`, `context`) node pairs through starting the biased random walk with a fixed length at per node. The starting nodes are taken as the target nodes and the following nodes in biased random walks are taken as context nodes. For each (`target`, `context`) node pair, we generate 1 negative node pair.\n",
    "\n",
    "2. Train the Node2Vec algorithm through minimizing cross-entropy loss for `target-context` pair prediction, with the predictive value obtained by performing the dot product of the 'input embedding' of the target node and the 'output embedding' of the context node, followed by a sigmoid activation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Specify the optional parameter values: the number of walks to take per node, the length of each walk. Here, to guarantee the running efficiency, we respectively set `walk_number` and `walk_length` to 100 and 5. Larger values can be set to them to achieve better performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "tags": [
     "parameters"
    ]
   },
   "outputs": [],
   "source": [
    "walk_number = 100\n",
    "walk_length = 5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create the biased random walker to perform context node sampling, with the specified parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "walker = BiasedRandomWalk(\n",
    "    G,\n",
    "    n=walk_number,\n",
    "    length=walk_length,\n",
    "    p=0.5,  # defines probability, 1/p, of returning to source node\n",
    "    q=2.0,  # defines probability, 1/q, for moving to a node away from the source node\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create the UnsupervisedSampler instance with the biased random walker."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "unsupervised_samples = UnsupervisedSampler(G, nodes=list(G.nodes()), walker=walker)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set the batch size and the number of epochs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 50\n",
    "epochs = 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define an attri2vec training generator, which generates a batch of (index of target node, index of context node, label of node pair) pairs per iteration."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "generator = Node2VecLinkGenerator(G, batch_size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Build the Node2Vec model, with the dimension of learned node representations set to 128."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "emb_size = 128\n",
    "node2vec = Node2Vec(emb_size, generator=generator)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_inp, x_out = node2vec.in_out_tensors()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use the link_classification function to generate the prediction, with the 'dot' edge embedding generation method and the 'sigmoid' activation, which actually performs the dot product of the 'input embedding' of the target node and the 'output embedding' of the context node followed by a sigmoid activation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "link_classification: using 'dot' method to combine node embeddings into edge embeddings\n"
     ]
    }
   ],
   "source": [
    "prediction = link_classification(\n",
    "    output_dim=1, output_act=\"sigmoid\", edge_embedding_method=\"dot\"\n",
    ")(x_out)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Stack the Node2Vec encoder and prediction layer into a Keras model. Our generator will produce batches of positive and negative context pairs as inputs to the model. Minimizing the binary crossentropy between the outputs and the provided ground truth is much like a regular binary classification task."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = keras.Model(inputs=x_inp, outputs=prediction)\n",
    "\n",
    "model.compile(\n",
    "    optimizer=keras.optimizers.Adam(lr=1e-3),\n",
    "    loss=keras.losses.binary_crossentropy,\n",
    "    metrics=[keras.metrics.binary_accuracy],\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Train the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train for 39760 steps\n",
      "Epoch 1/2\n",
      "39760/39760 [==============================] - 156s 4ms/step - loss: 0.2969 - binary_accuracy: 0.8529\n",
      "Epoch 2/2\n",
      "39760/39760 [==============================] - 240s 6ms/step - loss: 0.1087 - binary_accuracy: 0.9643\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(\n",
    "    generator.flow(unsupervised_samples),\n",
    "    epochs=epochs,\n",
    "    verbose=1,\n",
    "    use_multiprocessing=False,\n",
    "    workers=4,\n",
    "    shuffle=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualise Node Embeddings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Build the node based model for predicting node representations from node ids and the learned parameters. Below a Keras model is constructed, with `x_inp[0]` as input and `x_out[0]` as output. Note that this model's weights are the same as those of the corresponding node encoder in the previously trained node pair classifier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_inp_src = x_inp[0]\n",
    "x_out_src = x_out[0]\n",
    "embedding_model = keras.Model(inputs=x_inp_src, outputs=x_out_src)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get the node embeddings from node ids."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "50/50 [==============================] - 0s 1ms/step\n"
     ]
    }
   ],
   "source": [
    "node_gen = Node2VecNodeGenerator(G, batch_size).flow(subjects.index)\n",
    "node_embeddings = embedding_model.predict(node_gen, workers=4, verbose=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Transform the embeddings to 2d space for visualisation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "transform = TSNE  # PCA\n",
    "\n",
    "trans = transform(n_components=2)\n",
    "node_embeddings_2d = trans.fit_transform(node_embeddings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# draw the embedding points, coloring them by the target label (paper subject)\n",
    "alpha = 0.7\n",
    "label_map = {l: i for i, l in enumerate(np.unique(subjects))}\n",
    "node_colours = [label_map[target] for target in subjects]\n",
    "\n",
    "plt.figure(figsize=(7, 7))\n",
    "plt.axes().set(aspect=\"equal\")\n",
    "plt.scatter(\n",
    "    node_embeddings_2d[:, 0],\n",
    "    node_embeddings_2d[:, 1],\n",
    "    c=node_colours,\n",
    "    cmap=\"jet\",\n",
    "    alpha=alpha,\n",
    ")\n",
    "plt.title(\"{} visualization of node embeddings\".format(transform.__name__))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Node Classification\n",
    "In this task, we will use the `Node2Vec` node embeddings to train a classifier to predict the subject of a paper in Cora."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# X will hold the 128-dimensional input features\n",
    "X = node_embeddings\n",
    "# y holds the corresponding target values\n",
    "y = np.array(subjects)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Splitting\n",
    "\n",
    "We split the data into train and test sets. \n",
    "\n",
    "We use 10% of the data for training and the remaining 90% for testing as a hold-out test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Array shapes:\n",
      " X_train = (248, 128)\n",
      " y_train = (248,)\n",
      " X_test = (2237, 128)\n",
      " y_test = (2237,)\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.1, test_size=None)\n",
    "print(\n",
    "    \"Array shapes:\\n X_train = {}\\n y_train = {}\\n X_test = {}\\n y_test = {}\".format(\n",
    "        X_train.shape, y_train.shape, X_test.shape, y_test.shape\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Classifier Training\n",
    "\n",
    "We train a Logistic Regression classifier on the training data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegressionCV(Cs=10, class_weight=None, cv=10, dual=False,\n",
       "                     fit_intercept=True, intercept_scaling=1.0, l1_ratios=None,\n",
       "                     max_iter=300, multi_class='ovr', n_jobs=None, penalty='l2',\n",
       "                     random_state=None, refit=True, scoring='accuracy',\n",
       "                     solver='lbfgs', tol=0.0001, verbose=False)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = LogisticRegressionCV(\n",
    "    Cs=10, cv=10, scoring=\"accuracy\", verbose=False, multi_class=\"ovr\", max_iter=300\n",
    ")\n",
    "clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Predict the hold-out test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate the accuracy of the classifier on the test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7358068842199375"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbsphinx": "hidden",
    "tags": [
     "CloudRunner"
    ]
   },
   "source": [
    "<table><tr><td>Run the latest release of this notebook:</td><td><a href=\"https://mybinder.org/v2/gh/stellargraph/stellargraph/master?urlpath=lab/tree/demos/node-classification/keras-node2vec-node-classification.ipynb\" alt=\"Open In Binder\" target=\"_parent\"><img src=\"https://mybinder.org/badge_logo.svg\"/></a></td><td><a href=\"https://colab.research.google.com/github/stellargraph/stellargraph/blob/master/demos/node-classification/keras-node2vec-node-classification.ipynb\" alt=\"Open In Colab\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\"/></a></td></tr></table>"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
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 "nbformat": 4,
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